Event-driven fault diagnosis framework for automotive systems

ABSTRACT

Systems and methods for capturing and analyzing significant parameter data from vehicle systems whenever a diagnostic trouble code (DTC) is triggered. A multi-dimensional matrix is constructed, with vehicles, DTCs, and parameter data comprising three dimensions of the matrix. The data matrix is populated with DTC and parameter data from many different vehicles, either when vehicles are taken to a dealer for service, or via wireless data download. Time can be added as a fourth dimension of the matrix, providing an indication of whether a particular system or component is temporally degrading. When sufficient data is accumulated, the data matrix is pre-processed, features are extracted from the data, and the features are classified, using a variety of mathematical techniques. Trained classifiers are then used to diagnose the root cause of any particular fault signal, and also to provide a prognosis of system health and remaining useful life.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to a system and method for diagnosingfault conditions in a vehicle and, more particularly, to a system andmethod for capturing operating parameter data whenever a diagnostictrouble code (DTC) is triggered in a vehicle, and applying mathematicalmodels to both the parameter data and the DTC data to diagnose thereason for the fault condition.

2. Discussion of the Related Art

Modern vehicles are complex electrical and mechanical systems thatemploy many components, devices, modules, sub-systems, etc. that passoperating information between and among each other using sophisticatedalgorithms and data buses. As with anything, these types of devices andalgorithms are susceptible to errors, failures and faults that affectthe operation of the vehicle. When such errors and faults occur, oftenthe affected device or component will issue a fault code, such as adiagnostic trouble code (DTC), that is received by one or more systemcontroller identifying the fault, or some ancillary fault with anintegrated component. These DTCs can be analyzed by service techniciansand engineers to identify problems and/or make system corrections andupgrades. However, given the complexity of vehicle systems, many DTCsand other signals could be triggered for many different reasons, whichcould make trouble-shooting particularly difficult.

As mentioned above, modern vehicles have a number of mechanical andelectrical parts that are in electrical communication through variouscontrollers. If a certain actuator, sensor or sub-system is notoperating properly, the component or sub-system, or its controller, willtypically provide a DTC that is received by a system controller, suchthat the DTC can be either downloaded using telematics services, such asOnStar™, or diagnostic devices during service of the vehicle. However, aDTC can be triggered for a variety of reasons and based on manydifferent combinations of measured parameters. This often makes itdifficult to diagnose the true root cause of a problem based on a DTCvalue alone. This can result in the inability to find or repeat aproblem when the vehicle is being serviced, which leads to customerdissatisfaction, increased future warranty costs, and missedopportunities to improve system designs based on real-world failuremodes.

Given the unacceptably high rate of missed diagnoses of vehicle faultsusing current techniques, there is a need to improve the fault diagnosisof vehicle systems by capturing more operating parameter data when a DTCis triggered and applying advanced mathematical techniques to that datato find the true root cause of any fault condition.

SUMMARY OF THE INVENTION

In accordance with the teachings of the present invention, systems andmethods are disclosed for capturing and analyzing significant parameterdata from vehicle systems whenever a diagnostic trouble code (DTC) istriggered. A multi-dimensional data matrix is constructed, with vehiclesand DTCs comprising the first two dimensions, and a set of operatingparameter values, scan tool values, customer complaints, and text-basedsymptoms comprising the third dimension of the matrix. The data matrixis populated with DTC, operating parameter, and other data from manydifferent vehicles which have experienced fault events, either whenvehicles are taken to a dealer for service, or via telematics datadownload. Time can be added as a fourth dimension of the matrix,providing an indication of whether a particular system or component istemporally degrading. When sufficient data is accumulated, the datamatrix is pre-processed, features are extracted from the data, and theextracted features are classified, using a variety of mathematical andstatistical techniques. Trained classifiers are then used to diagnosethe root cause of other fault events, and also to provide a prognosis ofsystem health and remaining useful life.

Additional features of the present invention will become apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a vehicle with sensors and a controller forcapturing fault-related data to be used in a fault diagnosis framework;

FIG. 2 is a diagram of an event-driven data matrix used to organizefault-related data used for fault diagnosis;

FIG. 3 is a flow chart diagram of a process used for capturing vehiclefault event data and downloading it to a central computer;

FIG. 4 is a block diagram of a system used for a training phase of thefault diagnosis framework; and

FIG. 5 is a block diagram of a system used for a testing phase of thefault diagnosis framework.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following discussion of the embodiments of the invention directed toan event-driven fault diagnosis framework for automotive systems ismerely exemplary in nature, and is in no way intended to limit theinvention or its applications or uses. For example, the presentinvention has particular application for vehicle fault diagnosis.However, the method of the invention will have other applications forother industries, such as fault diagnosis in the aerospace, heavyequipment, and other transportation industries.

FIG. 1 is a diagram of a vehicle 12 with onboard equipment needed forcapturing fault-related data. The vehicle 12 includes an engine 14, andnumerous other systems and sub-systems, such as suspension, steering,transmission and driveline, thermal management, entertainment, andsecurity systems. Each of these systems is typically comprised of manysub-systems and components, which must be in good working condition inorder for the parent system to function properly. Any modern vehicle 12will include one or more controller 16, for monitoring and controllingthe various vehicle systems and sub-systems. The vehicle 12 will alsoinclude many onboard sensors 18 for measuring a wide range of system andcomponent parameters, including temperatures, pressures, voltages,forces, fluid flow rates, fluid levels, and others. The sensors 18 passtheir parameter data to the controller 16 via electronic networks, whichmay be wired or wireless. The controller 16 includes memory for storingparameter data, and a processor configured with algorithms forcontrolling the various vehicle systems and sub-systems.

In the vehicle 12, parameter data from the sensors 18 is monitoredcontinually. The controller 16 is programmed to check the values of manydifferent parameters and combinations of parameters, to see if thevalues are within normal ranges. If a parameter or combination ofparameters is detected to be outside its normal range, one or more faultcode, or diagnostic trouble code (DTC), is triggered and stored in thecontroller 16. For example, a fuel tank pressure sensor circuit which isreading out of its range may trigger a circuit-level DTC. This problemwould likely be fairly easily diagnosed as either a bad fuel tankpressure sensor or a faulty wiring connection from the fuel tankpressure sensor to the controller 16. On the other hand, an enginemisfire may trigger one or more subsystem-level DTCs. The misfirecondition may be very difficult to diagnose and correct, as it could becaused by a problem with the fuel or the fuel injection system, anignition system problem, a wiring problem, a mechanical problemsomewhere in the engine 14, or some other issue. The objective of thepresent invention is to capture as much relevant data as possiblewhenever a DTC is triggered, and analyze that data both for the purposeof resolving each individual vehicle problem and also for the purpose ofidentifying and correcting systemic design issues.

To achieve the desired objective, a three-dimensional data matrix isproposed. FIG. 2 is a diagram of a data matrix 30 used for collectingand organizing event-driven fault data. The data matrix 30 includesvehicles as a first dimension 32, DTCs as a second dimension 34, andparameter identification data (PID) as a third dimension 36. Vehiclescan be identified by their vehicle identification number (VIN), or anyother suitable identifier. DTCs must have a unique identifier ornumbering scheme. There may be hundreds of different DTCs associatedwith any particular vehicle platform. PIDs must also have a uniqueidentifier or numbering scheme. There are typically thousands ofparameters or PIDs that may be captured on any engine or vehicle family.Commonly used PIDs include engine rpm, vehicle speed, engine coolanttemperature, intake manifold pressure, fuel pressure, and batteryvoltage, for example. Throughout the ensuing discussion, the term PID isused generically to refer both to single parameter values, such asengine rpm, as well as to arrays or “packets” of related parameters,sometimes known as data packet identifiers (DPIDs).

In order to construct the data matrix 30, it is necessary to definewhich PIDs should be captured for each DTC. Many DTCs call for dozens,or even hundreds of specific PIDs to be captured. This relationship istypically defined by an engineer responsible for the system to which theDTC relates. Thus, the matrix 30 is constructed by defining for a givenvehicle platform which DTCs are applicable, and for each DTC which PIDsshould be captured. The data matrix 30 can then be populated with eventdata from individual vehicles in service when a DTC is triggered.

FIG. 3 is a flow chart diagram 40 of a process for capturing DTC and PIDdata and populating data matrices. The process begins at box 42 whereany particular vehicle's controller 16 is programmed with criteria fortriggering DTCs, and with a list of PIDs that should be captured whenany given DTC is triggered. For example, a particular DTC may be definedto trigger if certain combinations of conditions exist, such as engineoil pressure being below a certain value while engine rpm iscoincidentally within a certain range. As mentioned previously, hundredsof DTCs and their criteria are applicable to most vehicles. Also, foreach DTC, a set of PIDs is defined, such that all of the prescribed PIDdata is captured in a “freeze frame” fashion when any particular DTC istriggered. The process continues at box 44 when a vehicle 12 experiencesa fault event, or a problem which triggers one or more DTC. At box 46,the DTC(s) and associated PID data are captured in the controller 16 forthe event. At box 48, the individual event data is downloaded to acentral computer containing a master copy of the data matrix. The datadownload can occur when the vehicle 12 is taken to a dealer for service,or the data download can be handled at any time via a wirelesscommunication or telematics system. The activities of the boxes 44, 46,and 48 continue on an ongoing basis as more individual vehiclesexperience fault events, capture DTC and PID data, and download thatdata to the computer which hosts the master copy of the data matrix. Atbox 50, the master data matrix is populated with DTC and PID data fromdifferent vehicle events. The PID data can also be supplemented withother relevant information about the fault, such as data taken bydiagnostic tools at a service center, and customer feedback regardingthe event.

At this stage, the matrix contains data from events representing manydifferent vehicle system failure modes. It is then necessary to useactual field diagnosis data to segregate the event-specific DTC and PIDdata by failure mode. At box 52, field service reports, customerobservations, and other data are used to identify the actual failuremode, where possible, for each individual event data record. It is thenpossible to construct event-driven data matrices for specific failuremodes at box 54, where each failure-mode-specific data matrix containsthe vehicle, DTC, and PID data for events known to have one specificfailure mode. Failure-mode-specific data matrices can be constructed foras many different failure modes as data is available to support. Forexample, one failure-mode-specific data matrix may be populated with DTCand PID data for events which are diagnosed as being caused by a fueltank pressure (FTP) sensor low voltage failure, another matrix may becreated for events where the failure mode is diagnosed to be a FTPsensor high voltage failure, and so forth. After event-driven datamatrices are populated with sufficient data for several specific failuremodes, an analysis process may be undertaken.

The analysis process of the present invention takes place in twophases—a training phase, and a testing phase. In the training phase,various mathematical and statistical tools are used to analyze the datain the failure-mode-specific data matrices, identify characteristicfeatures of the data, and classify the data features by failure mode.The training phase includes a machine learning technique known astrained classifiers to identify failure-mode-specific patterns in thedata. In the testing phase, individual vehicle fault event data isanalyzed and diagnoses are rendered using the trained classifiers fromthe training phase. The testing phase validates with additional datathat the trained classifiers are operating as expected.

FIG. 4 is a block diagram of a system 60 used for the training phase.The training system 60 includes the mathematical and statistical toolsand techniques which can be used for preprocessing the data, extractingcharacteristic features of the data, and training the classifiers torecognize the data features by failure mode. It is described as atraining phase because the mathematical models actually learn whatparameter settings to use in order to extract the most relevant featuresof the data and properly classify the data features by actual failuremode. The training system 60 begins with failure-mode-specific datamatrices 62. As described above, each of the matrices 62 contains dataonly for events found to be attributed to one specific failure mode,where the data includes vehicle, DTC, and PID data. The data matrices 62are provided to a data preprocessing module 64, which usesnormalization, variance reduction, and possibly other techniques, toprepare the data matrices 62 for feature extraction. The purpose of thepreprocessing module 64 is to minimize or eliminate anomalies in thedata, such as errors that might occur during data download from avehicle to the central computer, which might skew or hinder the dataanalysis which is about to be undertaken.

A feature extraction module 66 uses several data reduction,transformation, and analysis techniques to identify features of the datafrom the data matrices 62. Features are characteristics of the datawhich may be useful for later classifying the data. Examples oftechniques used in the feature extraction module 66 include keystatistics, and dimension reduction techniques such as principalcomponent analysis (PCA), and partial least squares (PLS). Thesetechniques are all known to those skilled in the art of data analysisand digital signal processing. By automatically applying multipleanalysis techniques to the data, the feature extraction module 66 canidentify features which might otherwise be missed if only manual or lessextensive data analysis techniques were used.

A data classifier module 68 receives the features from the featureextraction module 66 and performs a classification. Classification is amachine learning technique used to predict group membership for datainstances. Broadly speaking, the goal of machine learning is for acomputer to automatically learn to recognize complex patterns and makeintelligent decisions based on data. As applied here, classificationwill be used to associate patterns in the data features with specificvehicle system failure modes. The data classifier module 68 uses manytypes of classifiers, including support vector machine (SVM),probabilistic neural networks (PNN), k-nearest neighbor (KNN), decisiontrees (DT), linear discriminant analysis (LDA), and quadraticdiscriminant analysis (QDA). These techniques, or combinations of themknown as fusions, are also known to those skilled in the art, and havebeen shown to be able to identify patterns in the data features whichare indicative of failure mode. The output of the classifier module 68is the cumulative learning of the entire training system 60. Thisincludes; the parameters and settings used in the data preprocessingmodule 64; the techniques and parameters used in, and the featuresresulting from, the feature extraction module 66; and the classifiersused, the parameters learned, and the classification patterns detectedin the classifier module 68.

The testing phase is used to validate the classifiers which weretrained, in a machine learning sense, in the training phase. That is,the testing phase will verify that the trained classifiers are effectivein identifying failure-mode-specific patterns in the data and reaching aproper diagnosis for additional event-based data. As a general rule ofthumb, of the total amount of event data available in thefailure-mode-specific data matrices 62, about two-thirds of it should beused for the training phase, and the remaining one-third should be usedfor the testing phase.

FIG. 5 is a block diagram of a system 80 used for the testing phase. Thetesting system 80 begins with a testing data matrix 82. The testing datamatrix 82 can include event data for multiple different known failuremode events, or it can contain only data for events known to be causedby a single specific failure mode. In either case, the testing phasewill be used to verify that the trained classifiers reach the properdiagnosis for each event in the testing data matrix 82. A datapreprocessing and feature extraction module 84 operates in the same wayas the modules 64 and 66 from the training system 60, using the sametechniques and settings used for feature extraction in the trainingsystem 60. This ensures that the features extracted from the testingdata matrix 82 will be comparable to the features extracted from thefailure-mode-specific data matrices 62 which were used in the trainingphase. Features extracted from the module 84 are passed to trainedclassifier module 86, which is configured with the parameters learnedduring the training phase. That is, the outcome of the training phase isthat specific classifiers have been trained, in a machine learningsense, to recognize patterns in the feature data associated withspecific failure modes. In the testing phase, the trained classifiersare used in the module 86 to diagnose failure modes for each specificevent in the testing data matrix 82.

Once the trained classifiers in the module 86 are tested and shown toreliably diagnose failure modes for specific DTC events, the featureextraction and classifier modules can be deployed, onboard vehicles orotherwise, for fault isolation. The most immediate benefit can be gainedby using the classifiers to diagnose the root causes of individualvehicle fault events as they occur. The classifiers can be applied tofault diagnosis in two ways. First, the diagnostic patterns can bedownloaded to computers 88 at vehicle service centers, so that when avehicle 12 which has experienced a fault event is brought to the servicecenter for repair, the DTC and PID data will be downloaded from thevehicle 12 to the service center computer 88, the diagnostic patternwill be recognized by the service center computer 88, and the diagnosiswill be provided to the service technician. Another way the dataclassification can be used for fault diagnosis is by downloading thediagnostic patterns directly to computers 90 onboard individual vehicles12. The computer 90 could be the same device as the controller 16, or itcould be a different device. If the onboard computer 90 is programmedwith the trained classifiers necessary to recognize diagnostic datapatterns, then when a fault event occurs and DTC and PID data iscaptured, it is possible for the onboard computer 90 to immediatelydiagnose the true root cause of the fault. This in turn would allowfurther action to be taken, such as reconfiguring or deactivatingfault-affected systems, or notifying the driver that the vehicle 12should be taken to a dealer soon for service. Wireless communication ortelematics systems could also be used to relay an onboard vehiclediagnosis, along with the associated DTC and PID data, to a vehicleservice center. The service center could then contact the vehicle'sowner, explain the situation to the owner, and possibly schedule anappointment for service.

The results of fault data analysis and classification can also beapplied to future product designs. For example, data analysis with themethods described above may reveal a recurring problem with thereliability or durability of a hardware component, or the operation of asoftware component, leading to a conclusion that the offending componentshould be re-designed for a future vehicle program. Applying fieldservice data from an existing fleet of vehicles to future vehicledesigns is nothing new, but the methods of the present invention allowfor improved diagnosis of the root cause of faults while reducing theincidence of mis-diagnosis, thus making it much more apparent whichfaults occur on which vehicles under what circumstances. With existingfault data analysis methods, some types of faults may never be diagnosedwith sufficient accuracy and frequency to enable a future designimprovement. For example, the offending component mentioned above maynever have been identified as being problematic previously, becausesystem faults may not have been properly diagnosed in many cases. Themethods of the present invention result in far clearer information whichcan be used in failure modes and effects analysis (FMEA) and applied tofuture vehicle designs.

The data matrix 30 can also be extended to a fourth dimension, with thefourth dimension being time. Time data can be measured in any suitabletime scale, preferably an absolute time scale which includes a year, aday number within the year, an hour, a minute, and a second. A timestamp would be recorded for every DTC event entry in the data matrix 30.Then, if a DTC event occurred for a second time on an individualvehicle, parameter data could be evaluated to see if anything changedsignificantly over time. For example, consider a fuel tank pressure(FTP) sensor intermittent DTC event which occurred thrice, with a monthof elapsed time between the three events. In this example, the DTC eventwas triggered when the engine control module detected an abrupt FTP PIDchange. If the FTP voltage PID has an increasing or decreasing trendduring these three events, this could indicate that either the FTPsensor reference port is getting obstructed, or that an FTP sensorcircuit low voltage or high voltage problem will be occurring in thenear future on that vehicle. This example illustrates in very simpleterms how the time data can be used to enhance the types of analysis anddiagnosis which are possible from the data matrix 30. Much moresophisticated analyses are possible, including using the temporal datato provide a prognosis of system health and remaining useful life.Remaining useful life data can then be used by a vehicle service centerto make a recommendation to a vehicle owner for repairing or replacingthe system or component which is degrading.

The methods and systems described above provide an improved diagnosisframework for vehicle system faults, while not requiring any additionalhardware to be included on vehicles. The improved fault diagnosisinformation can provide immediate benefit to a vehicle's owner throughrapid diagnosis and correction of any problem, and can enable a vehiclemanufacturer to improve customer satisfaction, reduce warranty costs,and improve future product designs.

The foregoing discussion discloses and describes merely exemplaryembodiments of the present invention. One skilled in the art willreadily recognize from such discussion and from the accompanyingdrawings and claims that various changes, modifications and variationscan be made therein without departing from the spirit and scope of theinvention as defined in the following claims.

1. A method for diagnosing causes of faults in vehicle systems, saidmethod comprising: defining a plurality of diagnostic trouble codeswhich are applicable to various fault conditions in a vehicle's systems,criteria for triggering each diagnostic trouble code, and a list ofparameter identification data which should be captured for eachdiagnostic trouble code; providing a controller onboard a vehicle whichis programmed with the diagnostic trouble codes and the criteria fortriggering each diagnostic trouble code, and which is also programmedwith the list of parameter identification data which should be capturedfor each diagnostic trouble code; providing sensors onboard the vehicleas needed to capture the parameter identification data; capturing thediagnostic trouble code and parameter identification data by thecontroller when a fault condition occurs in a vehicle system, where eachfault condition occurrence is known as a fault event; downloading thediagnostic trouble code and parameter identification data from thecontroller on the vehicle to a central computer; storing diagnostictrouble code and parameter identification data for fault events frommultiple vehicles on the central computer, where each fault event has anactual failure mode attributed to it; analyzing the diagnostic troublecode and parameter identification data from the fault events stored onthe central computer using mathematical models which can learn tocorrelate features in the data with the actual failure mode for eachfault event; and using the mathematical models to diagnose a failuremode for additional fault events.
 2. The method of claim 1 whereinanalyzing the diagnostic trouble code and parameter identification dataincludes a training phase and a testing phase.
 3. The method of claim 2wherein the training phase includes: providing input data comprising amatrix of data for each of a plurality of failure modes, where eachmatrix of data identifies the failure mode to which it pertains, andincludes diagnostic trouble code and parameter identification data forfault events from a plurality of vehicles; using a pre-processing moduleto normalize and reduce variation in the input data; using a featureextraction module to extract characteristic features of the input data;using a classifier module to identify patterns in the features of theinput data and associate the patterns with the actual failure mode; andstoring techniques and settings used in the feature extraction moduleand the classifier module which most effectively correlate the inputdata with the actual failure modes.
 4. The method of claim 3 wherein thefeature extraction module includes at least one of the techniquescomprising key statistics, principal component analysis, and partialleast squares.
 5. The method of claim 3 wherein the classifier moduleincludes at least one of the techniques comprising support vectormachine, probabilistic neural networks, k-nearest neighbor, decisiontrees, linear discriminant analysis, and quadratic discriminantanalysis.
 6. The method of claim 3 wherein the testing phase includes:providing a testing data matrix which includes diagnostic trouble codeand parameter identification data for fault events from a plurality ofvehicles, where each fault event has the actual failure mode attributedto it; using the pre-processing module, the feature extraction module,the classifier module, and the stored techniques and settings from thetraining phase to produce a diagnosed failure mode for each fault event;and verifying that the stored techniques and settings from the trainingphase are effective with the testing data matrix by comparing thediagnosed failure mode with the actual failure mode for each faultevent.
 7. The method of claim 6 further comprising downloading themathematical models after the training and testing phases to a computerat a vehicle repair service center, and using the mathematical models todiagnose a failure mode for fault events on vehicles which are broughtto the vehicle repair service center for diagnosis.
 8. The method ofclaim 6 further comprising downloading the mathematical models after thetraining and testing phases to a computer onboard a vehicle, and usingthe mathematical models to diagnose a failure mode for fault events onsaid vehicle.
 9. The method of claim 1 wherein the diagnostic troublecode and parameter identification data also includes a time stamp foreach fault event.
 10. The method of claim 9 further comprising using thediagnostic trouble code and parameter identification data and the timestamp for each fault event to make a prognosis of system health andremaining useful life for any system involved in the fault event. 11.The method of claim 1 wherein the actual failure mode for each faultevent is provided by a vehicle repair service center which has resolvedthe fault condition.
 12. A method for diagnosing causes of faults andmaking a prognosis of system health in vehicle systems, said methodcomprising: defining a plurality of diagnostic trouble codes which areapplicable to various fault conditions in a vehicle's systems, criteriafor triggering each diagnostic trouble code, and a list of parameteridentification data which should be captured for each diagnostic troublecode; providing a controller onboard a vehicle which is programmed withthe diagnostic trouble codes and the criteria for triggering eachdiagnostic trouble code, and which is also programmed with the list ofparameter identification data which should be captured for eachdiagnostic trouble code; providing sensors onboard the vehicle as neededto capture the parameter identification data; capturing the diagnostictrouble code and parameter identification data by the controller when afault condition occurs in a vehicle system, where each fault conditionoccurrence is known as a fault event, and data captured for each faultevent also includes a time stamp; downloading the diagnostic troublecode and parameter identification data and time stamps from thecontroller on the vehicle to a central computer; storing diagnostictrouble code and parameter identification data and time stamps for faultevents from multiple vehicles on the central computer, where each faultevent has an actual failure mode attributed to it; analyzing thediagnostic trouble code and parameter identification data from the faultevents stored on the central computer using mathematical models whichcan learn to correlate features in the data with the actual failure modefor each fault event; using the mathematical models to diagnose afailure mode for additional fault events; and using the diagnostictrouble code and parameter identification data and the time stamp foreach fault event to make a prognosis of system health and remaininguseful life for any system involved in the fault event.
 13. The methodof claim 12 wherein analyzing the diagnostic trouble code and parameteridentification data includes a training phase, where the training phaseincludes: providing input data comprising a matrix of data for each of aplurality of failure modes, where each matrix of data identifies thefailure mode to which it pertains, and includes diagnostic trouble codeand parameter identification data for fault events from a plurality ofvehicles; using a pre-processing module to normalize and reducevariation in the input data; using a feature extraction module toextract characteristic features of the input data, where the featureextraction module includes at least one of the techniques comprising keystatistics, principal component analysis, and partial least squares;using a classifier module to identify patterns in the features of theinput data and associate the patterns with the actual failure mode,where the classifier module includes at least one of the techniquescomprising support vector machine, probabilistic neural networks,k-nearest neighbor, decision trees, linear discriminant analysis, andquadratic discriminant analysis; and storing techniques and settingsused in the feature extraction module and the classifier module whichmost effectively correlate the input data with the actual failure modes.14. The method of claim 13 wherein analyzing the diagnostic trouble codeand parameter identification data includes a testing phase, where thetesting phase includes: providing a testing data matrix which includesdiagnostic trouble code and parameter identification data for faultevents from a plurality of vehicles, where each fault event has theactual failure mode attributed to it; using the pre-processing module,the feature extraction module, the classifier module, and the storedtechniques and settings from the training phase to produce a diagnosedfailure mode for each fault event; and verifying that the storedtechniques and settings from the training phase are effective with thetesting data matrix by comparing the diagnosed failure mode with theactual failure mode for each fault event.
 15. A system for diagnosingcauses of faults in vehicle systems, said system comprising: a vehiclewith a plurality of systems to be monitored; a plurality of sensorsonboard the vehicle for measuring various conditions in the systems; acontroller onboard the vehicle, where the controller is configured toreceive parameter data from the sensors, monitor the parameter data todetermine if any fault conditions exist, and capture and store aplurality of parameter data when a fault event occurs; a centralcomputer for collecting parameter data for fault events from a pluralityof vehicles, where the computer uses mathematical models to analyze theparameter data to diagnose failure modes for groups of fault events andfor individual fault events; and a communications system onboard thevehicle for transmitting the parameter data for fault events to thecentral computer.
 16. The system of claim 15 wherein the controllerincludes a pre-defined list of diagnostic trouble codes, criteria fortriggering each diagnostic trouble code, and parameter data to captureand store for each diagnostic trouble code.
 17. The system of claim 15wherein the communications system is a wireless system which cantransmit the parameter data to the central computer without the vehiclehaving to be taken to a vehicle repair service center.
 18. The system ofclaim 15 wherein the mathematical models used by the central computerinclude data pre-processing, feature extraction, and trained classifiermodules.
 19. The system of claim 18 wherein: the data pre-processingmodule includes normalization and variance reduction techniques; thefeature extraction module includes key statistics, principal componentanalysis, and partial least squares techniques; and the trainedclassifier module includes support vector machine, probabilistic neuralnetworks, k-nearest neighbor, decision trees, linear discriminantanalysis, and quadratic discriminant analysis techniques.
 20. The systemof claim 15 further comprising a computer onboard the vehicle which isconfigured to use the mathematical models from the central computer todiagnose a failure mode for any new fault event which occurs on thevehicle's systems.